package de.ugoe.cs.cpdp.dataprocessing;

import org.apache.commons.collections4.list.SetUniqueList;

import weka.core.Instances;
import weka.filters.Filter;
import weka.filters.supervised.instance.Resample;

/**
 * Implements oversampling, a strategy for
 * handling bias in data. In case there are less positive samples (i.e.
 * defect-prone) samples in the data than negative samples (i.e.
 * non-defect-prone), the defect-prone entities are over-sampled such that the
 * number of defect-prone and non-defect-prone instances is the same afterwards.
 * This means, that some of the defect-prone entities will be more than once
 * within the data.
 * 
 * @author Steffen Herbold
 */
public class Oversampling implements IProcessesingStrategy,
		ISetWiseProcessingStrategy {

	/**
	 * Does not have parameters. String is ignored.
	 * 
	 * @param parameters
	 *            ignored
	 */
	@Override
	public void setParameter(String parameters) {
		// dummy
	}

	/*
	 * (non-Javadoc)
	 * 
	 * @see
	 * de.ugoe.cs.cpdp.dataprocessing.ISetWiseProcessingStrategy#apply(weka.
	 * core.Instances, org.apache.commons.collections4.list.SetUniqueList)
	 */
	@Override
	public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
		for (Instances traindata : traindataSet) {
			apply(testdata, traindata);
		}
	}

	/*
	 * (non-Javadoc)
	 * 
	 * @see
	 * de.ugoe.cs.cpdp.dataprocessing.IProcessesingStrategy#apply(weka.core.
	 * Instances, weka.core.Instances)
	 */
	@Override
	public void apply(Instances testdata, Instances traindata) {

		final int[] counts = traindata.attributeStats(traindata.classIndex()).nominalCounts;
		if (counts[1] < counts[0]) {
			Instances negatives = new Instances(traindata);
			Instances positives = new Instances(traindata);

			for (int i = traindata.size() - 1; i >= 0; i--) {
				if (Double.compare(1.0, negatives.get(i).classValue()) == 0) {
					negatives.remove(i);
				}
				if (Double.compare(0.0, positives.get(i).classValue()) == 0) {
					positives.remove(i);
				}
			}

			Resample resample = new Resample();
			// TODO: resample.setSampleSizePercent((100.0*counts[1])/100+0.01);
			// Ohne +0.01 wird bei tomcat, xerces-1.2 und jedit-4.0 ein negative
			// weniger zurückgegeben
			resample.setSampleSizePercent((100.0 * counts[0]) / counts[1]);
			try {
				resample.setInputFormat(traindata);
				positives = Filter.useFilter(positives, resample);
			} catch (Exception e) {
				throw new RuntimeException(e);
			}
			traindata.clear();
			for (int i = 0; i < negatives.size(); i++) {
				traindata.add(negatives.get(i));
			}
			for (int i = 0; i < positives.size(); i++) {
				traindata.add(positives.get(i));
			}
		}
	}

}